Learn-Then-Differentiate Gradient Estimation
Learn-then-differentiate (LTD) estimates gradients by fitting a model to simulation outputs and differentiating it. We develop a unified framework explaining what LTD differentiates and how accurately it estimates gradients. For models with a weighted representation, LTD differentiates a learned representation of the underlying probability measure. We then show how accuracy guarantees for fitted models translate into guarantees for gradients and higher-order derivatives, with rates approaching the standard Monte Carlo rate under suitable smoothness conditions. The framework recovers established results for kernel regression, local polynomial regression, and kernel ridge regression, and yi...
| Authors | Nifei Lin, Qingkai Zhang, L. Jeff Hong |
|---|---|
| Primary category | stat.CO |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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